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Click here to search FindAPhD.com for PhD studentship opportunities(MBRC) Integration of streaming data to improve opportunities for antimicrobial treatments in patients with serious life-threatening infections (sepsis)
Prof Paul Dark, Prof T Van Staa, Dr Tim Felton
No more applications being accepted
Funded PhD Project (European/UK Students Only)
About the Project
To develop and evaluate software algorithms to analyse continuously, collected patient level clinical data, laboratory biomarkers of infection, microbiological results, treatment and outcome data for patient and population benefit.
Antibiotics are important medicines for treating bacterial infections in both humans and animals and are losing their effectiveness at an increasing rate. Antibiotic resistance is one of the most significant threats to patients’ safety. It is driven by overusing antibiotics. To slow down the development of antibiotic resistance it is important to use antibiotics in the right way, to use the right drug, at the right dose, at the right time for the right duration. There is great need for effective and simple interventions that optimise antibiotic prescribing. A recent review concluded that we need to better understand the quality of interventions in this area and what works best when. The NHS faces very different populations and healthcare setting and these may all respond differently to the introduction of new interventions. But the conventional scientific approach is to evaluate single interventions in well-controlled identical circumstances without capturing the real-world complexity of the NHS. This project will focus on the important clinical challenge of optimising antibiotics in patients with life-threatening infection in hospitals
Our aim through this project is to develop and evaluate software algorithms to integrate relevant, real-world NHS data sources that drive clinical decisions about antimicrobial treatment of hospitalised patients with serious infections.
Antibiotics are important medicines for treating bacterial infections in both humans and animals and are losing their effectiveness at an increasing rate. Antibiotic resistance is one of the most significant threats to patients’ safety. It is driven by overusing antibiotics. To slow down the development of antibiotic resistance it is important to use antibiotics in the right way, to use the right drug, at the right dose, at the right time for the right duration. There is great need for effective and simple interventions that optimise antibiotic prescribing. A recent review concluded that we need to better understand the quality of interventions in this area and what works best when. The NHS faces very different populations and healthcare setting and these may all respond differently to the introduction of new interventions. But the conventional scientific approach is to evaluate single interventions in well-controlled identical circumstances without capturing the real-world complexity of the NHS. This project will focus on the important clinical challenge of optimising antibiotics in patients with life-threatening infection in hospitals
Our aim through this project is to develop and evaluate software algorithms to integrate relevant, real-world NHS data sources that drive clinical decisions about antimicrobial treatment of hospitalised patients with serious infections.
Funding Notes
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in Data Science, Mathematics, Statistics, Epidemiology or other degrees with demonstrated and considerable focus on quantitative research method.
Applicants must be from the UK/EU and funding covers fees/stipend for three years commencing September 2018. Applicants may contact the Primary Supervisor directly with any questions. Online applications must be submitted, select 'Manchester BRC' as the programme - for more information on how to apply please visit https://www.bmh.manchester.ac.uk/study/research/funded-programmes/mbrc-studentships/
Applicants must be from the UK/EU and funding covers fees/stipend for three years commencing September 2018. Applicants may contact the Primary Supervisor directly with any questions. Online applications must be submitted, select 'Manchester BRC' as the programme - for more information on how to apply please visit https://www.bmh.manchester.ac.uk/study/research/funded-programmes/mbrc-studentships/
References
1. Chief Medical Officer annual report 2011: antimicrobial resistance (http://media.dh.gov.uk/network/357/files/2013/03/CMO-Annual-Report-Volume-2-20111.pdf)
2. Bielicki JA et al. Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data. J Antimicrob Chemother. 2016 71(3):794-802.
3. BRIT – Using data to tackle antibiotic resistance (https://www.connectedhealthcities.org/research-projects/using-data-tackle-antibiotic-resistance/)
4. Start Smart - Then Focus. Antimicrobial Stewardship Toolkit for English Hospitals. Public Health England. (https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/417032/Start_Smart_Then_Focus_FINAL.PDF)
5. Antimicrobial stewardship: systems and processes for effective antimicrobial medicine use. National Institute for Health and Care Excellence. (https://www.nice.org.uk/guidance/ng15)
2. Bielicki JA et al. Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data. J Antimicrob Chemother. 2016 71(3):794-802.
3. BRIT – Using data to tackle antibiotic resistance (https://www.connectedhealthcities.org/research-projects/using-data-tackle-antibiotic-resistance/)
4. Start Smart - Then Focus. Antimicrobial Stewardship Toolkit for English Hospitals. Public Health England. (https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/417032/Start_Smart_Then_Focus_FINAL.PDF)
5. Antimicrobial stewardship: systems and processes for effective antimicrobial medicine use. National Institute for Health and Care Excellence. (https://www.nice.org.uk/guidance/ng15)
